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Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models 出版年份: 2024 作者: Daniel Van Niekerk | Maria Peréz Ortiz | John Shaw-Taylor | Davor Orlic | Ivana Drobnjak | Jackie Kay | Noah Siegel | Katherine Evans | Nyalleng Moorosi | Tina Eliassi-Rad | Leone Maria Tanczer | Wayne Holmes | Marc Peter Deisenroth | Isabel Straw | Maria Fasli | Rachel Adams | Nuria Oliver | Dunja Mladenić | Urvashi Aneja | Madeleine Janicky 机构作者: UNESCO | International Research Centre on Artificial Intelligence (IRCAI) This study explores biases in three significant large language models (LLMs): OpenAI’s GPT-2 and ChatGPT, along with Meta’s Llama 2, highlighting their role in both advanced decision-making systems and as user-facing conversational agents. Across multiple studies, the brief reveals how biases emerge in the text generated by LLMs, through gendered word associations, positive or negative regard for gendered subjects, or diversity in text generated by gender and culture. The research uncovers persistent social biases within these state-of-the-art language models, despite ongoing efforts to mitigate such issues. The findings underscore the critical need for continuous research and policy intervention to address the biases that exacerbate as these technologies are integrated across diverse societal and cultural landscapes. The emphasis on GPT-2 and Llama 2 being open-source foundational models is particularly noteworthy, as their widespread adoption underlines the urgent need for scalable, objective methods to assess and correct biases, ensuring fairness in AI systems globally.